Max Schäfer

Ellen Arteca, Max Schäfer, Frank Tip

Event-driven programming is widely practiced in the JavaScript community, both on the client side to handle UI events and AJAX requests, and on the server side to accommodate long-running operations such as file or network I/O. Many popular event-based APIs allow event names to be specified as free-form strings without any validation, potentially leading to lost events for which no listener has been registered and dead listeners for events that are never emitted. In previous work, Madsen et al. presented a precise static analysis for detecting such problems, but their analysis does not scale because it may require a number of contexts that is exponential in the size of the program. Concentrating on the problem of detecting dead listeners, we present an approach to learn how to correctly use event-based APIs by first mining a large corpus of JavaScript code using a simple static analysis to identify code snippets that register an event listener, and then applying statistical modeling to identify anomalous patterns, which often indicate incorrect API usage. From a large-scale evaluation on 127,531 open-source JavaScript code bases, our technique was able to detect 75 anomalous listener-registration patterns, while maintaining a precision of 90.9% and recall of 7.5% over our validation set, demonstrating that a learning-based approach to detecting event-handling bugs is feasible. In an additional experiment, we investigated instances of these patterns in 25 open-source projects, and reported 30 issues to the project maintainers, of which 7 have been confirmed as bugs.

Adriana Sejfia, Max Schäfer

The npm registry is one of the pillars of the JavaScript and TypeScript ecosystems, hosting over 1.7 million packages ranging from simple utility libraries to complex frameworks and entire applications. Due to the overwhelming popularity of npm, it has become a prime target for malicious actors, who publish new packages or compromise existing packages to introduce malware that tampers with or exfiltrates sensitive data from users who install either these packages or any package that (transitively) depends on them. Defending against such attacks is essential to maintaining the integrity of the software supply chain, but the sheer volume of package updates makes comprehensive manual review infeasible. We present Amalfi, a machine-learning based approach for automatically detecting potentially malicious packages comprised of three complementary techniques. We start with classifiers trained on known examples of malicious and benign packages. If a package is flagged as malicious by a classifier, we then check whether it includes metadata about its source repository, and if so whether the package can be reproduced from its source code. Packages that are reproducible from source are not usually malicious, so this step allows us to weed out false positives. Finally, we also employ a simple textual clone-detection technique to identify copies of malicious packages that may have been missed by the classifiers, reducing the number of false negatives. Amalfi improves on the state of the art in that it is lightweight, requiring only a few seconds per package to extract features and run the classifiers, and gives good results in practice: running it on 96287 package versions published over the course of one week, we were able to identify 95 previously unknown malware samples, with a manageable number of false positives.

Saikat Dutta, Diego Garbervetsky, Shuvendu Lahiri et al.

Static analysis has established itself as a weapon of choice for detecting security vulnerabilities. Taint analysis in particular is a very general and powerful technique, where security policies are expressed in terms of forbidden flows, either from untrusted input sources to sensitive sinks (in integrity policies) or from sensitive sources to untrusted sinks (in confidentiality policies). The appeal of this approach is that the taint-tracking mechanism has to be implemented only once, and can then be parameterized with different taint specifications (that is, sets of sources and sinks, as well as any sanitizers that render otherwise problematic flows innocuous) to detect many different kinds of vulnerabilities. But while techniques for implementing scalable inter-procedural static taint tracking are fairly well established, crafting taint specifications is still more of an art than a science, and in practice tends to involve a lot of manual effort. Past work has focussed on automated techniques for inferring taint specifications for libraries either from their implementation or from the way they tend to be used in client code. Among the latter, machine learning-based approaches have shown great promise. In this work we present our experience combining an existing machine-learning approach to mining sink specifications for JavaScript libraries with manual taint modelling in the context of GitHub's CodeQL analysis framework. We show that the machine-learning component can successfully infer many new taint sinks that either are not part of the manual modelling or are not detected due to analysis incompleteness. Moreover, we present techniques for organizing sink predictions using automated ranking and code-similarity metrics that allow an analysis engineer to efficiently sift through large numbers of predictions to identify true positives.